Preemptive chassis control intervention for autonomous vehicle

ABSTRACT

Described herein is a system and method for preemptive chassis control intervention for an autonomous vehicle having a mechanical system and a chassis controller. The chassis controller is configured to output a consumption signal that represents a percentage of an activation threshold consumed by an operation monitored by the chassis controller, wherein the chassis controller is activated to manipulate the mechanical system when the activation threshold is reached. A computing system of the autonomous vehicle receives the consumption signal output by the chassis controller to determine a path plan for the autonomous vehicle based upon the percentage of the activation threshold consumed by the operation monitored by the chassis controller. The computing system further controls the mechanical system to execute the path plan to preempt activation of the chassis controller.

BACKGROUND

An autonomous vehicle is a motorized vehicle that can navigate without ahuman driver. An exemplary autonomous vehicle includes a plurality ofsensor systems, such as, but not limited to, a camera sensor system, alidar sensor system, a radar sensor system, amongst others, wherein theautonomous vehicle operates based upon sensor signals output by thesensor systems. The sensor signals are typically provided to a computingsystem in communication with the plurality of sensor systems, whereinthe computing system outputs a control signal for controlling amechanical system of the autonomous vehicle, such as a vehiclepropulsion system, a braking system, or a steering system.

Additionally, the autonomous vehicle may include one or more chassiscontrollers, such as an antilock braking system (ABS), a tractioncontrol system (TCS), or an electronic stability control (ESC). Thechassis controllers may be activated to manipulate operations of themechanical systems independent of control signals output by thecomputing system. Activation of a chassis controller may be based uponan operation monitored by the chassis controller, such as wheel orvehicle stability, whereas a control signal may be output by thecomputing system to execute a path plan. Accordingly, execution of somecontrol signals may cause a chassis controller to activate. For example,understeering on a low friction surface may cause the computing systemto output a control signal for turning the wheels even farther in orderto follow the path plan. However, execution of such a control signal maycause the ESC to activate when a more desirable action for themechanical system is to counter-steer the autonomous vehicle.

While a chassis controller can be incorporated in an autonomous vehicleto assist performance of maneuvers executed in accordance with controlsignals from the computing system, activation of a chassis controllermay detrimentally impact passenger comfort, cause deviations from anidentified path plan, and/or result in a degraded state of theautonomous vehicle. Moreover, chassis controllers are usually configuredas independent systems from the computing system that generates thecontrol signals for maneuvering the autonomous vehicle. As a result,chassis controllers typically do not communicate with the computingsystem to provide indications of an impending chassis controlleractivation; accordingly, conventional computing systems of autonomousvehicles commonly are unable to modify path plans or manipulationactuations of the mechanical systems based on impending chassiscontroller activation.

SUMMARY

The following is a brief summary of subject matter that is described ingreater detail herein. This summary is not intended to be limiting as tothe scope of the claims.

Described herein are various technologies pertaining to a system andmethod of preemptive chassis control intervention for an autonomousvehicle. With more specificity, described herein is a computing systemthat interfaces with a chassis controller to identify an impendingchassis controller activation. With still more specificity, describedherein is an autonomous vehicle comprising a mechanical system and achassis controller in communication with a computing system of theautonomous vehicle. The computing system generates control signals tocontrol actuation of the mechanical system. The chassis controller isactivated to manipulate the actuation of the mechanical system when anactivation threshold of the chassis controller is reached. Further, thechassis controller is configured to output a consumption signal thatrepresents a percentage of the activation threshold consumed by anoperation monitored by the chassis controller. The consumption signaloutput by the chassis controller is received by the computing system anda path plan for the autonomous vehicle is determined based upon thepercentage of the activation threshold consumed by the operationmonitored by the chassis controller specified by the consumption signal,wherein the mechanical system is controlled by the computing system toexecute the path plan, for example, by manipulating brake pressure,brake torque, propulsion torque, steering angle, etc.

According to various embodiments, the chassis controller can comprise aprocessor and memory for determining the percentage of the activationthreshold consumed by the operation monitored by the chassis controllerand transmitting, from the chassis controller, the consumption signalindicative of the percentage of the activation threshold consumed by themonitored operation. In accordance with such embodiments, the chassiscontroller can be any of an ABS, a TCS, an ESC, amongst others. Thechassis controller and the computing system may communicate with eachother via a controller area network (CAN) bus incorporated in theautonomous vehicle.

The autonomous vehicle can include a plurality of chassis controllers,for example. Following this example, a first chassis controller, such asthe ABS, can have a first activation threshold, a second chassiscontroller, such as the TCS, can have a second activation threshold, anda third chassis controller, such as the ESC, can have a third activationthreshold. That is, the ABS is activated when the first activationthreshold is reached, the TCS is activated when the second threshold isreached, and the ESC is activated when the third threshold is reached.Accordingly, the computing system determines from the consumption signalwhether a percentage of the activation threshold of a particular chassiscontroller exceeds a preemptive action threshold. The preemptive actionthreshold represents the percentage of the activation threshold at whichthe path plan is modified to preempt activation of the chassiscontroller. Specifically, when the preemptive action threshold isexceeded, the path plan is modified at one or more impending timesteps,wherein modification of the path plan causes actuation of the mechanicalsystem at the one or more impending timesteps to be manipulated toreduce the percentage of the activation threshold below the activationthreshold of the particular chassis controller.

Additionally or alternatively, pursuant to various other embodiments,the activation threshold can be a variable threshold. The variablethreshold is a threshold that may be modified by the chassis controllerat periodic intervals based upon information provided by the computingsystem. For example, data input indicative of an environmentalcondition, vehicle speed, traffic congestion, etc. may be received bythe computing system to manipulate the threshold at which the chassiscontroller is activated. A percentage of the variable threshold consumedby an operation monitored by the chassis controller is represented in aconsumption signal received by the computing system, similar to theconsumption signal received by the computing system generated for afixed activation threshold.

In a further embodiment, it is contemplated that a surface friction canbe identified for generating a path plan of the autonomous vehicle. Inparticular, the computing system is configured to receive dataindicative of an actuation force of the mechanical system (e.g., aconstant 500 Nm of brake force). An increase in the percentage of theactivation threshold can be identified from the consumption signal,wherein the increase corresponds to a change in the percentage of theactivation threshold over time. The surface friction may then bedetermined from the change in the percentage of the activation thresholdover time and the data indicative of the actuation force of themechanical system to control the mechanical system based upon thesurface friction. Alternatively, the consumption signal can include dataindicative of the surface friction, wherein the path plan may bedetermined based upon the data indicative of the surface friction.

The above summary presents a simplified summary in order to provide abasic understanding of some aspects of the systems and/or methodsdiscussed herein. This summary is not an extensive overview of thesystems and/or methods discussed herein. It is not intended to identifykey/critical elements or to delineate the scope of such systems and/ormethods. Its sole purpose is to present some concepts in a simplifiedform as a prelude to the more detailed description that is presentedlater.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary autonomous vehicle.

FIG. 2 illustrates an exemplary autonomous vehicle including a pluralityof chassis controllers.

FIG. 3 illustrates an exemplary environment including a chassiscontroller that outputs a consumption signal to a computing system basedupon operations monitored by the chassis controller.

FIG. 4 illustrates an exemplary environment including a chassiscontroller that establishes a variable threshold based upon inputsprovided to a computing system.

FIG. 5 is an exemplary flow diagram illustrating preemptive chassiscontroller intervention.

FIG. 6 illustrates an exemplary tire mu slip curve.

FIG. 7 is a flow diagram illustrating an exemplary methodology forcontrolling a mechanical system based upon a consumption signal.

FIG. 8 is a flow diagram illustrating an exemplary methodology forproviding a consumption signal from a chassis controller.

FIG. 9 illustrates an exemplary computing system.

DETAILED DESCRIPTION

Various technologies pertaining to preemptive chassis controlintervention for an autonomous vehicle is now described with referenceto the drawings, wherein like reference numerals are used to refer tolike elements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of one or more aspects. It may be evident,however, that such aspect(s) may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order to facilitate describing one or moreaspects. Further, it is to be understood that functionality that isdescribed as being carried out by certain system components may beperformed by multiple components. Similarly, for instance, a componentmay be configured to perform functionality that is described as beingcarried out by multiple components.

Moreover, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom the context, the phrase “X employs A or B” is intended to mean anyof the natural inclusive permutations. That is, the phrase “X employs Aor B” is satisfied by any of the following instances: X employs A; Xemploys B; or X employs both A and B.

In addition, the articles “a” and “an” as used in this application andthe appended claims should generally be construed to mean “one or more”unless specified otherwise or clear from the context to be directed to asingular form.

Further, as used herein, the terms “component”, “module”, and “system”are intended to encompass computer-readable data storage that isconfigured with computer-executable instructions that cause certainfunctionality to be performed when executed by a processor. Thecomputer-executable instructions may include a routine, a function, orthe like. It is also to be understood that a component, module, orsystem may be localized on a single device or distributed across severaldevices.

Further, as used herein, the term “exemplary” is intended to meanserving as an illustration or example of something and is not intendedto indicate a preference.

Terms such as “first” and “second” are used herein. It is to beappreciated that these terms are used for purposes of identifyingdifferent items of the same kind (e.g., a first chassis controller and asecond chassis controller), and are not necessarily meant to convey somesort of ordering or relative comparison between the different items.

With reference now to FIG. 1, an exemplary autonomous vehicle 100 isillustrated. The autonomous vehicle 100 can navigate about roadwayswithout a human driver based upon sensor signals output by sensorsystems 102-104 of the autonomous vehicle 100. The autonomous vehicle100 includes a plurality of sensor systems 102-104 (a first sensorsystem 102 through an Nth sensor system 104). The sensor systems 102-104are of different types and are arranged about the autonomous vehicle100. For example, the first sensor system 102 may be a camera sensorsystem and the Nth sensor system 104 may be a lidar sensor system. Otherexemplary sensor systems include, but are not limited to, radar sensorsystems, global positioning system (GPS) sensor systems, inertialmeasurement units (IMU), infrared sensor systems, laser sensor systems,sonar sensor systems, and the like. Furthermore, some or all of the ofsensor systems 102-104 may be articulating sensors that can beoriented/rotated such that a field of view of the articulating sensorsis directed towards different regions surrounding the autonomous vehicle100.

The autonomous vehicle 100 further includes several mechanical systemsthat can be used to effectuate appropriate motion of the autonomousvehicle 100. For instance, the mechanical systems can include but arenot limited to, a vehicle propulsion system 106, a braking system 108,and a steering system 110. The vehicle propulsion system 106 may includean electric motor, an internal combustion engine, or both. The brakingsystem 108 can include an engine break, brake pads, actuators, and/orany other suitable componentry that is configured to assist indecelerating the autonomous vehicle 100. The steering system 110includes suitable componentry that is configured to control thedirection of movement of the autonomous vehicle 100 during propulsion.

The autonomous vehicle 100 additionally includes a chassis controller122 that is activated to manipulate the mechanical systems 106-110 whenan activation threshold of the chassis controller 122 is reached. Thechassis controller 122 is further configured to output a consumptionsignal that represents a percentage of the activation threshold consumedby an operation monitored by the chassis controller, such as brakepressure, brake torque, propulsion torque, steering angle, etc. Inparticular, the activation threshold can be represented as anormalized/non-dimensional value indicative of a current consumption ofthe activation threshold of the chassis controller 122. For example, ifthe current brake torque is at 50% of the activation threshold of thechassis controller 122, the normalized/non-dimensional value may berepresented as 0.5. It is to be appreciated from the foregoing, however,that the claimed subject matter is not limited to a single chassiscontroller or a specific type of chassis controllers and that anadditional number of chassis controllers or other types of chassiscontrollers may be incorporated in the autonomous vehicle 100 tomanipulate actuation of the mechanical systems 106-110 when anactivation threshold is reached.

The autonomous vehicle 100 further comprises a computing system 112 thatis in communication with the sensor systems 102-104, the mechanicalsystems 106-110, and the chassis controller 122. While the chassiscontroller 122 is activated independently from operations of thecomputing system 112, the chassis controller 122 may be configured tocommunicate with the computing system 112, for example, via a controllerarea network (CAN) bus 124 to provide the consumption signal forpreempting chassis controller activations. The computing system 112includes a processor 114 and memory 116 that stores instructions whichare executed by the processor 114 to cause the processor 114 to performacts in accordance with the instructions.

The memory 116 comprises a path planning system 118 and a control system120. The path planning system 118 generates a path plan for theautonomous vehicle 100, wherein the path plan can be identified bothspatially and temporally according to one or more impending timesteps.The path plan can include one or more maneuvers to be performed by theautonomous vehicle 100. The chassis controller 122 can continuouslycalculate the percentage of the activation threshold consumed by anoperation of the autonomous vehicle 100 based upon metrics such asvehicle speed, vehicle acceleration, wheel state, etc., wherein anintervention by the chassis controller 122 occurs when the percentage ofthe activation threshold represented in the consumption signal reaches avalue of 100% or higher. The percentage of the activation threshold maylikewise be represented on a scale of 0 to 1.0, wherein 1.0 isindicative of 100% of the activation threshold.

The consumption signal output by the chassis controller 122 is receivedby the path planning system 118 to determine, based upon the percentageof the activation threshold represented in the consumption signal,whether a current path plan should be modified. For instance, when theautonomous vehicle 100 transitions from a low mu surface to a higher musurface, the percentage of the activation threshold represented in theconsumption signal may increase (e.g., from 20% to 50%). When thepercentage of the activation threshold exceeds a predetermined value(e.g., 50% consumption of the activation threshold) as a result of thechange in surface mu, the path planning system 118 can evaluate thecurrent path plan in one or more impending timesteps, where projectedactuations of the mechanical systems 106-110 are preplanned, todetermine whether execution of the current path plan can continuewithout an intervention from the chassis controller 122 or whether amodified path plan is desirable for the autonomous vehicle 100 to avoidan intervention from the chassis controller 122. Projected actuations ofthe mechanical systems 106-110 can be based upon the percentage of theactivation threshold in relation to the timesteps as well as an actuatorrequest derivative.

In an example, if actuation of the braking system 108 causes a constantbrake force of 500 Nm during a time period that the percentage of theactivation threshold increased from 20% to 50%, the path planning system118 can identify that the surface friction has decreased. Thus, the pathplanning system 118 can evaluate projected actuations of the mechanicalsystems 106-110 at the timestep+1, the timestep+2, etc., to determine ifpreplanned actuations will continue to increase the percentage of theactivation threshold toward 100%. If so, the path planning system 118may determine that the path plan can be modified in a manner thatfacilitates a reduction in brake pressure, which can reduce thepercentage of the activation threshold consumed by operation of thebraking system 108. Otherwise, the path planning system 118 may leavethe current path plan unmodified. While the foregoing example describespreemptive controls for the braking system 108, manipulation of themechanical systems 106-110 to avoid a chassis controller interventionbased upon a consumption signal is not limited to a specific type ofchassis controller 122.

The control system 120 is configured to control the mechanical systemsof the autonomous vehicle 100 (e.g., the vehicle propulsion system 106,the brake system 108, and the steering system 110) based upon an outputfrom the sensor systems 102-104 and/or the path planning system 118. Forinstance, the mechanical systems can be controlled by the control system120 to execute the path plan determined by the path planning system 118.Additionally or alternatively, the control system 120 may control themechanical systems 106-110 to navigate the autonomous vehicle 100 inaccordance with outputs received from the sensor systems 102-104.

With reference now to FIG. 2, the autonomous vehicle 200 may comprise aplurality of chassis controllers 202-206, which can include an antilockbraking system (ABS) 202, a traction control system (TCS) 204, and anelectronic stability control (ESC) 206. The ABS 202 and the TCS 204 areactivated to manipulate longitudinal movement and the ESC 206 isactivated to manipulate lateral movement when their respectiveactivation thresholds are reached. For example, the ABS 202 can beactivated to reduce brake pressure from the braking system 108 to avoidlocking or skidding the wheels when a brake torque is identified thatmay cause the surface friction of the road to be exceeded. Similarly,the TCS 204 can be activated to reduce propulsion torque from thevehicle propulsion system 106 or increase brake torque from the brakingsystem 108 when a propulsive torque is identified that may cause thesurface friction of the road to be exceeded. The ESC 206 can beactivated to manipulate the steering system 110 when the yaw errorbetween the autonomous vehicle 200 and the steering angle causesundesirable oversteering or understeering. Activation of the ESC 206 mayfurther result in increasing the brake pressure from the braking system108 to stabilize lateral movement of the autonomous vehicle 200. Thus,the mechanical systems 106-110 may cause data to be generated for thechassis controllers 202-206 that is indicative of actuation of themechanical systems 106-110 and the chassis controllers 202-206 maycontrol the mechanical systems 106-110 based upon the generated data.

The chassis controllers 202-206 are each configured to output aconsumption signal that represents a percentage of their respectiveactivation thresholds consumed by one or more operation monitored by thechassis controllers 202-206, such as brake pressure, brake torque,propulsion torque, steering angle, etc. For example, the ABS 202 mayhave a first activation threshold, the TCS 204 may have a secondactivation threshold, and the ESC 206 may have a third activationthreshold, wherein the ABS 202 is activated when the first activationthreshold is reached, the TCS 204 is activated when the second thresholdis reached, and the ESC 206 is activated when the third threshold isreached.

A computing system 112 of the autonomous vehicle 200 is in communicationwith the chassis controllers 202-206 via a CAN bus 124. The CAN bus 124may communicate one or more consumption signals from the chassiscontrollers 202-206 to the computing system 112. The computing system112 is likewise configured to control the mechanical systems 106-110based upon the percentage of the activation threshold represented in theconsumption signal to preempt activation of the chassis controllers202-206.

With reference now to FIG. 3, environment 300 illustrates a chassiscontroller 122 that receives data indicative of operations of themechanical systems, such as the vehicle propulsion system 106, thebraking system 108, and/or the steering system 110, and outputs aconsumption signal to the computing system 112 based upon the data. Thechassis controller 122 includes a processor 302 and memory 304 thatstores instructions which are executed by the processor 302 to cause theprocessor 302 to perform acts in accordance with the instructions. Inparticular, memory 304 comprises a consumption module 306 configured todetermine a percentage of an activation threshold consumed by anoperation monitored by the chassis controller, such as brake pressure,brake torque, propulsion torque, steering angle, etc., wherein thechassis controller is activated when the activation threshold isreached. For example, if the autonomous vehicle passes over a low musurface, such as a wet manhole cover, while a constant brake torque isbeing applied by the braking system 108, rapid angular deceleration ofthe wheel may be indicative of a change in surface friction that causesthe chassis controller 122 to activate. The consumption module 306 isconfigured to identify the percentage of the activation threshold, forexample, based upon the rate of angular deceleration of the wheel andtransmit, from the chassis controller, a consumption signal indicativeof the percentage of the activation threshold to computing system 112.

With reference now to FIG. 4, environment 400 illustrates a chassiscontroller 402 (e.g., the chassis controller 122) that determines anactivation threshold, wherein the activation threshold can be a variablethreshold based upon one or more inputs provided to a computing system112. For instance, the variable threshold may be based upon inputsindicative of environmental conditions, vehicle speed, trafficcongestion, geographic data, amongst others. Some of the inputs may bereceived by the computing system 112 from sensors incorporated in theautonomous vehicle 416, such as an environment sensor 412 that sensesenvironmental conditions around the autonomous vehicle 416 (e.g., snow,rain, ambient temperature, etc.) and/or a speed sensor 414 that sensesthe speed of the autonomous vehicle 416. Additionally or alternatively,inputs received by the computing system 112 may be provided from a datastore 420 of a remote computing system 418. The data store 420 caninclude traffic data 422 that identifies, for example, current orhistorical data indicative of traffic congestion as well as geographicdata 424 that can be indicative of current or historical weatherpatterns for a specified geographic area. Accordingly, inputs receivedby the computing system 112 may be provided to the chassis controller402 over the CAN bus 124 for the chassis controller 402 to determine avariable threshold based upon the inputs.

The chassis controller 402 includes a processor 404 and memory 406 thathave similar functionality to the processor 302 and memory 304. Memory406 includes a threshold determination module 408 and a consumptionmodule 410. The threshold determination module 408 is configured todetermine an activation threshold for the chassis controller 402 basedupon information received via the CAN bus 124. Specifically, theactivation threshold may be a variable threshold that is periodicallymodified by the chassis controller 402 based upon the informationprovided by the computing system 112. For example, if the thresholddetermination module 408 is provided with information indicative ofweather conditions that are 85 degrees and sunny, the thresholddetermination module 408 can establish a higher activation threshold fora chassis controller 402, such as an ABS, than may otherwise beestablished if weather conditions were identified as 20 degrees andsnowing. The consumption module 410 is configured to identify apercentage of the variable threshold consumed by an operation monitoredby the chassis controller 402, such as braking, and generate aconsumption signal that represents the percentage of the variablethreshold. The consumption signal representing the percentage of thevariable threshold is received by the computing system 112 over the CANbus 124.

In an exemplary embodiment, the activation threshold may be representedas a fixed threshold and the percentage of the activation threshold atwhich the mechanical systems are controlled to preempt an interventionby the chassis controller 402 may be varied by the computing system 112.For example, if the computing system 112 receives an input indicative ofweather conditions that are 85 degrees and sunny, the computing system112 may determine that the percentage of the activation thresholdconsumed by an operation of the autonomous vehicle 416 can reach 75%before preemptive action is needed. In contrast, if the computing system112 receives an input indicative of weather conditions that are 20degrees and snowing, the computing system may determine that preemptiveaction is desirable at 30% consumption of the activation threshold.

With reference now to FIG. 5, an exemplary flow diagram 500 illustratespreemptive chassis control intervention. The flow diagram 500 starts at502, and at 504-510 activation thresholds are determined for a pluralityof chassis controller types. Specifically, a first activation thresholdis determined for the ABS at 504, a second activation threshold isdetermined for the TCS at 506, a third activation threshold isdetermined for the ESC at 508, and an Mth activation threshold isdetermined for an Mth chassis controller type at 510. At 512, operationsof an autonomous vehicle are monitored by the chassis controllers. Theoperations can be based upon brake pressure, brake torque, propulsiontorque, steering angle, and the like. At 514, a percentage of theactivation threshold consumed by the operations monitored by the chassiscontrollers is determined. A different percentage of the activationthreshold may be determined for each of the chassis controllers.

At 516, the chassis controller is activated when the percentage of theactivation threshold reaches 100%, wherein the flow diagram 500 maycomplete at 528. Alternatively, the chassis controller does not activateat 516 when the percentage of the activation threshold is less than100%. Instead, a consumption signal is generated at 518 that representsthe percentage of the activation threshold consumed by the operationmonitored by the chassis controller. Additionally, a consumption signalmay be generated at 518 even when the chassis controller is activated at516 that represents a percentage of the activation threshold of 100% orgreater.

The consumption signal generated at 518 may be provided to a computingsystem of an autonomous vehicle where, at 520, a path plan can bedetermined for the autonomous vehicle. The path plan represents spatialand temporal positions of the autonomous vehicle at one or moreimpending timesteps. At 522, it is determined from the consumptionsignal whether the percentage of the activation threshold exceeds apreemptive action threshold. A preemptive action threshold is athreshold that represents the percentage of the activation threshold atwhich the path plan is modified to preempt activation of the chassiscontroller. When the preemptive action threshold is exceeded by thepercentage of the activation threshold, the path plan is modified, at524, at the one or more impending timesteps, wherein modification of thepath plan causes actuation of the mechanical system at the one or moreimpending timesteps to be manipulated. The modified path plan isexecuted at 526. If the percentage of the activation threshold does notexceed the preemptive action threshold, the current/unmodified path plandetermined at 520 is executed at 526. The flow diagram 500 completes at528 upon execution of a path plan at 526.

With reference now to FIG. 6, an exemplary tire mu slip curve 600 isillustrated, wherein peak friction corresponds to a critical slip valueindicative of actuation of the braking system. Accordingly, a chassiscontroller may be configured to output a consumption signal thatincludes data indicative of a surface friction by detecting a percentageof tire slip and correlating the percentage of tire slip to theexemplary tire mu slip curve 600. The data indicative of the surfacefriction may be received by a computing system to determine a path planfor an autonomous vehicle by utilizing the identified surface frictiondata to avoid activation of the chassis controller.

In instances where the chassis controller is not configured to providethe computing system with a projected surface friction, the surfacefriction may still be determined for controlling the mechanical systemsof the autonomous vehicle based upon a change in the percentage of theactivation threshold over time and data indicative of an actuation forceof the mechanical systems. That is, the computing system can receivedata indicative of an actuation force applied by the mechanical system,such as 500 Nm of brake force, and further identify an increase in thepercentage of the activation threshold represented in the consumptionsignal, wherein the increase corresponds to a change in the percentageof the activation threshold over time. Since a chassis controller, suchas an ABS, may be configured to activate at or near the peak of theexemplary tire mu slip curve 600, identifying the brake force and aslope that corresponds to the change in the percentage of the activationthreshold over time provides a basis for projecting the surface frictionand manipulating the path plan of the autonomous vehicle.

FIGS. 7 and 8 illustrate exemplary methodologies relating to preemptivechassis control intervention. While the methodologies are shown anddescribed as being a series of acts that are performed in a sequence, itis to be understood and appreciated that the methodologies are notlimited by the order of the sequence. For example, some acts can occurin a different order than what is described herein. In addition, an actcan occur concurrently with another act. Further, in some instances, notall acts may be required to implement a methodology described herein.

Moreover, the acts described herein may be computer-executableinstructions that can be implemented by one or more processors and/orstored on a computer-readable medium or media. The computer-executableinstructions can include a routine, a sub-routine, programs, a thread ofexecution, and/or the like. Still further, results of acts of themethodologies can be stored in a computer-readable medium, displayed ona display device, and/or the like.

Referring now to FIG. 7, an exemplary methodology 700 is illustrated foroperating an autonomous vehicle having a mechanical system and a chassiscontroller. The methodology 700 starts at 702, and at 704 a consumptionsignal generated by a chassis controller is received, for example, by acomputing system of the autonomous vehicle in communication with thechassis controller. The consumption signal represents a percentage of anactivation threshold consumed by an operation monitored by the chassiscontroller, wherein the chassis controller is activated to manipulatethe mechanical system of the autonomous vehicle when the activationthreshold is reached. At 706, a path plan is determined for theautonomous vehicle based upon the percentage of the activation thresholdrepresented in the consumption signal. At 708, the mechanical system ofthe autonomous vehicle is controlled to execute the path plan. Themethodology 700 completes at 710.

Referring now to FIG. 8, an exemplary methodology 800 is illustrated forproviding a consumption signal from a chassis controller. Themethodology 800 starts at 802, and at 804 a percentage of an activationthreshold consumed by an operation monitored by the chassis controlleris determined, wherein the chassis controller is activated when theactivation threshold is reached. The operation monitored by the chassiscontroller may be indicative of wheel or vehicle stability. At 806, aconsumption signal is transmitted from the chassis controller indicativeof the percentage of the activation threshold consumed by the operationmonitored by the chassis controller. The consumption signal may bereceived by a computing system that controls a mechanical system of anautonomous vehicle. The methodology 800 completes at 808.

Referring now to FIG. 9, a high-level illustration of an exemplarycomputing device 900 that can be used in accordance with the systems andmethodologies disclosed herein is illustrated. For instance, thecomputing device 900 may be or include the computing system 112. Thecomputing device 900 includes at least one processor 902 that executesinstructions that are stored in a memory 904.

The instructions may be, for instance, instructions for implementingfunctionality described as being carried out by one or more modules andsystems discussed above or instructions for implementing one or more ofthe methods described above. In addition to storing executableinstructions, the memory 904 may also store location information,distance information, direction information, etc.

The computing device 900 additionally includes a data store 908 that isaccessible by the processor 902 by way of the system bus 906. The datastore 908 may include executable instructions, location information,distance information, direction information, etc. The computing device900 also includes an input interface 910 that allows external devices tocommunicate with the computing device 900. For instance, the inputinterface 910 may be used to receive instructions from an externalcomputer device, etc. The computing device 900 also includes an outputinterface 912 that interfaces the computing device 900 with one or moreexternal devices. For example, the computing device 900 may transmitcontrol signals to the vehicle propulsion system 106, the braking system108, and/or the steering system 110 by way of the output interface 912.

Additionally, while illustrated as a single system, it is to beunderstood that the computing device 900 may be a distributed system.Thus, for instance, several devices may be in communication by way of anetwork connection and may collectively perform tasks described as beingperformed by the computing device 900.

Various functions described herein can be implemented in hardware,software, or any combination thereof. If implemented in software, thefunctions can be stored on or transmitted over as one or moreinstructions or code on a computer-readable medium. Computer-readablemedia includes computer-readable storage media. A computer-readablestorage media can be any available storage media that can be accessed bya computer. By way of example, and not limitation, suchcomputer-readable storage media can comprise RAM, ROM, EEPROM, CD-ROM orother optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium that can be used to store desiredprogram code in the form of instructions or data structures and that canbe accessed by a computer. Disk and disc, as used herein, includecompact disc (CD), laser disc, optical disc, digital versatile disc(DVD), floppy disk, and Blu-ray disc (BD), where disks usually reproducedata magnetically and discs usually reproduce data optically withlasers. Further, a propagated signal is not included within the scope ofcomputer-readable storage media. Computer-readable media also includescommunication media including any medium that facilitates transfer of acomputer program from one place to another. A connection, for instance,can be a communication medium. For example, if the software istransmitted from a website, server, or other remote source using acoaxial cable, fiber optic cable, twisted pair, digital subscriber line(DSL), or wireless technologies such as infrared, radio, and microwave,then the coaxial cable, fiber optic cable, twisted pair, DSL, orwireless technologies such as infrared, radio and microwave are includedin the definition of communication medium. Combinations of the aboveshould also be included within the scope of computer-readable media.

Alternatively, or in addition, the functionally described herein can beperformed, at least in part, by one or more hardware logic components.For example, and without limitation, illustrative types of hardwarelogic components that can be used include Field-programmable Gate Arrays(FPGAs), Application-specific Integrated Circuits (ASICs),Application-specific Standard Products (ASSPs), System-on-a-chip systems(SOCs), Complex Programmable Logic Devices (CPLDs), etc.

What has been described above includes examples of one or moreembodiments. It is, of course, not possible to describe everyconceivable modification and alteration of the above devices ormethodologies for purposes of describing the aforementioned aspects, butone of ordinary skill in the art can recognize that many furthermodifications and permutations of various aspects are possible.Accordingly, the described aspects are intended to embrace all suchalterations, modifications, and variations that fall within the spiritand scope of the appended claims. Furthermore, to the extent that theterm “includes” is used in either the detailed description or theclaims, such term is intended to be inclusive in a manner similar to theterm “comprising” as “comprising” is interpreted when employed as atransitional word in a claim.

What is claimed is:
 1. An autonomous vehicle, comprising: a mechanicalsystem; a chassis controller configured to manipulate the mechanicalsystem upon being activated, wherein the chassis controller is activatedto manipulate the mechanical system when an activation threshold isreached, wherein the chassis controller is not activated when theactivation threshold is not reached, wherein the chassis controller isfurther configured to output a consumption signal representative of anon-dimensional value indicative of a relationship between a detectedvalue of a parameter monitored by the chassis controller and theactivation threshold; and a computing system in communication with themechanical system and the chassis controller, wherein the computingsystem comprises: a processor; and memory that stores instructions that,when executed by the processor, cause the processor to perform actscomprising: receiving the consumption signal output by the chassiscontroller; determining a path plan for the autonomous vehicle basedupon the non-dimensional value indicative of a relationship between thedetected value of the parameter monitored by the chassis controller andthe activation threshold such that the path plan mitigates a projectedactivation of the chassis controller; and controlling the mechanicalsystem to execute the path plan.
 2. The autonomous vehicle of claim 1,wherein the chassis controller is at least one of an antilock brakingsystem controller, a traction control system controller, or anelectronic stability control controller.
 3. The autonomous vehicle ofclaim 1, further comprising a controller area network bus, wherein thechassis controller and the computing system communicate via thecontroller area network bus.
 4. The autonomous vehicle of claim 1,wherein the activation threshold is a variable threshold based upon atleast one of an environmental condition, a vehicle speed, or trafficcongestion.
 5. The autonomous vehicle of claim 1, wherein the activationthreshold is a variable threshold that is periodically modified by thechassis controller based upon information provided by the computingsystem.
 6. The autonomous vehicle of claim 1, wherein the acts performedby the processor further comprise: determining from the consumptionsignal whether the non-dimensional value exceeds a preemptive actionthreshold; and modifying the path plan at one or more impendingtimesteps responsive to the preemptive action threshold being exceededby the non-dimensional value, wherein modification of the path plancauses actuation of the mechanical system at the one or more impendingtimesteps to be manipulated.
 7. The autonomous vehicle of claim 1,wherein controlling the mechanical system to execute the path planincludes manipulating at least one of brake pressure, brake torque,propulsion torque, or steering angle.
 8. The autonomous vehicle of claim1, wherein the chassis controller is at least one of an antilock brakingsystem controller having a first activation threshold, a tractioncontrol system controller having a second activation threshold, or anelectronic stability control controller having a third activationthreshold; and wherein the antilock braking system controller isactivated when the first activation threshold is reached, the tractioncontrol system controller is activated when the second activationthreshold is reached, and the electronic stability control controller isactivated when the third activation threshold is reached.
 9. Theautonomous vehicle of claim 1, wherein the acts performed by theprocessor further comprise: receiving data indicative of an actuationforce of the mechanical system; identifying an increase in thenon-dimensional value from the consumption signal, wherein the increasecorresponds to a change in the non-dimensional value over time;determining a surface friction based upon the change in thenon-dimensional value over time and the data indicative of the actuationforce of the mechanical system; and controlling the mechanical systembased upon the surface friction.
 10. The autonomous vehicle of claim 1,wherein the consumption signal includes data indicative of a surfacefriction, and wherein the path plan is further determined based upon thedata indicative of the surface friction.
 11. A method of operating anautonomous vehicle having a mechanical system and a chassis controller,the method comprising: receiving a consumption signal outputted by thechassis controller, wherein the consumption signal represents anon-dimensional value indicative of a relationship between a detectedvalue of a parameter monitored by the chassis controller and anactivation threshold, wherein the chassis controller manipulates themechanical system upon being activated, wherein the chassis controlleris activated to manipulate the mechanical system when the activationthreshold is reached, and wherein the chassis controller is notactivated when the activation threshold is not reached; determining apath plan for the autonomous vehicle based upon the non-dimensionalvalue indicative of a relationship between the detected value of theparameter monitored by the chassis controller and the activationthreshold such that the path plan mitigates a projected activation ofthe chassis controller; and controlling the mechanical system to executethe path plan.
 12. The method of claim 11, further comprisingrepresenting information in the consumption signal received from atleast one of an antilock braking system controller, a traction controlsystem controller, or an electronic stability control controller. 13.The method of claim 11, wherein the consumption signal is received fromthe chassis controller at a computing system of the autonomous vehiclevia a controller area network bus.
 14. The method of claim 11, furthercomprising varying the activation threshold based upon at least one ofan environmental condition, a vehicle speed, or traffic congestion. 15.The method of claim 11, further comprising: determining from theconsumption signal whether the non-dimensional value exceeds apreemptive action threshold; and modifying the path plan at one or moreimpending timesteps responsive to the preemptive action threshold beingexceeded by the non-dimensional value, wherein modification of the pathplan causes actuation of the mechanical system at the one or moreimpending timesteps to be manipulated.
 16. The method of claim 11,wherein controlling the mechanical system to execute the path planfurther comprises manipulating at least one of brake pressure, braketorque, propulsion torque, or steering angle.
 17. The method of claim11, wherein the chassis controller is at least one of an antilockbraking system controller having a first activation threshold, atraction control system controller having a second activation threshold,or an electronic stability control controller having a third activationthreshold; and wherein the antilock braking system controller isactivated when the first activation threshold is reached, the tractioncontrol system controller is activated when the second activationthreshold is reached, and the electronic stability control controller isactivated when the third activation threshold is reached.
 18. The methodof claim 11, further comprising: receiving data indicative of anactuation force of the mechanical system; identifying an increase in thenon-dimensional value from the consumption signal, wherein the increasecorresponds to a change in the non-dimensional value over time;determining a surface friction based upon the change in thenon-dimensional value over time and the data indicative of the actuationforce of the mechanical system; and controlling the mechanical systembased upon the surface friction.